Run gemma-4-31B-it-qat-w4a16-ct Locally via Ollama 2 with 1M Context
If you need a near-instant local setup, just fetch files via a basic curl request.
Follow the guidelines below to continue.
The setup auto-downloads all needed files (several GBs).
Once launched, the wizard detects your specs to configure the model for maximum efficiency.
The Gemma-4-31B-it-qat-w4a16-ct is a large language model designed for instruction following and conversational tasks. It leverages 31 billion parameters to achieve a balance between accuracy and computational efficiency. The model employs QAT (quantized aware training) combined with a w4a16 format, enabling reduced memory footprint while preserving performance. Its CT architecture incorporates advanced attention mechanisms that improve context retention and response relevance. The following table summarizes key technical attributes.
| Parameter Count | 31 B |
| Quantization | QAT (w4a16) |
| Precision | 16‑bit float |
| Training Method | Instruction‑following fine‑tuning |
| Architecture | CT with enhanced attention |
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